Title:
Incorporating Complex Statistical Information in Active Contour-Based Image Segmentation
Incorporating Complex Statistical Information in Active Contour-Based Image Segmentation
Author(s)
Kim, Junmo
Fisher, John W., III
Çetin, Müjdat
Yezzi, Anthony
Willsky, Alan S.
Fisher, John W., III
Çetin, Müjdat
Yezzi, Anthony
Willsky, Alan S.
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Abstract
An information-theoretic method for multiphase image segmentation, in an active contour-based framework is proposed. Our approach is based on nonparametric density estimates, and is able to solve problems involving arbitrary probability densities for the region intensities. This is achieved by maximizing the mutual information between the region labels and the image pixel intensities, in order to segment up to 2m regions using m curves. The method does not require any prior training regarding the regions of interest, but rather learns the probability densities during the evolution process. We present some illustrative experimental results, demonstrating the power of the proposed segmentation approach.
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Date Issued
2003-09
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Text
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Proceedings